{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Federated Learning: Create Multi-Armed Bandit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up Sandbox...\n",
      "Done!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f16f10927f0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import syft as sy\n",
    "from syft.serde import protobuf\n",
    "from syft_proto.execution.v1.plan_pb2 import Plan as PlanPB\n",
    "from syft_proto.execution.v1.state_pb2 import State as StatePB\n",
    "from syft.grid.clients.model_centric_fl_client import ModelCentricFLClient\n",
    "from syft.execution.state import State\n",
    "from syft.execution.placeholder import PlaceHolder\n",
    "from syft.execution.translation import TranslationTarget\n",
    "\n",
    "import torch as th\n",
    "from torch import nn\n",
    "\n",
    "import os\n",
    "import websockets\n",
    "import json\n",
    "import requests\n",
    "\n",
    "sy.make_hook(globals())\n",
    "hook.local_worker.framework = None\n",
    "th.random.manual_seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rand_rates [0.0885472705142738, 0.6573085565027963, 0.12284517599193097, 0.7990040078449123, 0.791515274873796, 0.7951421774517767, 0.7964242039032917, 0.2342354669402873, 0.13484465589758438, 0.7943876988091906, 0.42902378833295013, 0.6323957990003999, 0.20454880915194337, 0.7944278969774013, 0.7951043193420357, 0.7933400109605253, 0.7635500510530244, 0.4951194491427903, 0.7292068670766164, 0.797757742535327, 0.15224863549623446, 0.790581520948647, 0.09704308430144826, 0.44637770307031815]\n",
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] 24\n",
      "0\n",
      "samples_from_beta_distr {0: 0.25772215174333984, 1: 0.9895674342078271, 2: 0.8250782803891253, 3: 0.3200182940751499, 4: 0.6991780894188369, 5: 0.17003515929852706, 6: 0.2794315124820956, 7: 0.5950993407127924, 8: 0.7930306447804777, 9: 0.8952979951839822, 10: 0.3354580410853247, 11: 0.06756618325560014, 12: 0.09203734302957793, 13: 0.053545121542020874, 14: 0.19032579304639544, 15: 0.8958725361896626, 16: 0.06603773367112277, 17: 0.84248362651063, 18: 0.4271005118891515, 19: 0.4365904160024805, 20: 0.6920575479509858, 21: 0.8050575818116107, 22: 0.8897866109690176, 23: 0.33600118926967915}\n",
      "selected action:  1 rwd:  1\n",
      "updated rewd vec:  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  0 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.]) tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "samples_from_beta_distr {0: 0.5661824435582796, 1: 0.890009462783495, 2: 0.9374656572930693, 3: 0.8497722020552476, 4: 0.5289606899806287, 5: 0.6041068016647505, 6: 0.38511061079273823, 7: 0.034581026881656325, 8: 0.8427389776314306, 9: 0.9424494296520215, 10: 0.7295788245927748, 11: 0.3506976021173679, 12: 0.6954967933681315, 13: 0.4387700451658261, 14: 0.9330992576305001, 15: 0.9058285201779508, 16: 0.3438733615195364, 17: 0.8836465567572842, 18: 0.18974438290238732, 19: 0.5690090523660288, 20: 0.03220747245381193, 21: 0.48900892319256006, 22: 0.5279031674505877, 23: 0.04642720690204379}\n",
      "selected action:  9 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  1 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.]) tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "2\n",
      "samples_from_beta_distr {0: 0.5924777307589026, 1: 0.4235433410263969, 2: 0.5390899789899907, 3: 0.8519049462575208, 4: 0.5856101774891526, 5: 0.4683453590194847, 6: 0.06544741111027108, 7: 0.9155333872838696, 8: 0.77919431355532, 9: 0.5729834360948853, 10: 0.09274007163279054, 11: 0.9893678825875754, 12: 0.1263237478893062, 13: 0.2535494564741812, 14: 0.3412480473092834, 15: 0.7366433154026605, 16: 0.3445860717633729, 17: 0.21656279403728648, 18: 0.06757559154598848, 19: 0.9225823813179393, 20: 0.7711303184583523, 21: 0.8116349438564516, 22: 0.24787626936968654, 23: 0.13818398991759046}\n",
      "selected action:  11 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  2 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.]) tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "3\n",
      "samples_from_beta_distr {0: 0.6991281723478953, 1: 0.6993524604986968, 2: 0.4995646553734048, 3: 0.8008826855518913, 4: 0.7249238472924162, 5: 0.2645541872508591, 6: 0.449961452221353, 7: 0.4966032385847375, 8: 0.893380892318763, 9: 0.022541897909544032, 10: 0.14049717258812705, 11: 0.655802014775209, 12: 0.8241463348399781, 13: 0.22772509535177676, 14: 0.5314918876746818, 15: 0.5780415857513486, 16: 0.5043770805659559, 17: 0.3588305680375512, 18: 0.785856323970624, 19: 0.015001984970656006, 20: 0.2984024296385773, 21: 0.38354713740166346, 22: 0.9516850036419292, 23: 0.03345861024799583}\n",
      "selected action:  22 rwd:  1\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]\n",
      "time_step:  3 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "4\n",
      "samples_from_beta_distr {0: 0.9138676613424035, 1: 0.911722813299459, 2: 0.7516249269345219, 3: 0.7849485831365813, 4: 0.4741694093242638, 5: 0.4873756892531516, 6: 0.6935842613100646, 7: 0.6913902820419738, 8: 0.08064471699785576, 9: 0.05203061415640796, 10: 0.3344801088572107, 11: 0.6609412890195216, 12: 0.7870831360623568, 13: 0.9699695701791468, 14: 0.9359168451582555, 15: 0.6886296446717234, 16: 0.1598546165741838, 17: 0.5461612460412063, 18: 0.7601712124393115, 19: 0.2797534198033927, 20: 0.6834464575212633, 21: 0.46601360673973635, 22: 0.7886767470810071, 23: 0.23651217306327396}\n",
      "selected action:  13 rwd:  1\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  4 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "5\n",
      "samples_from_beta_distr {0: 0.9710319403759009, 1: 0.8583124938064326, 2: 0.01686940750180598, 3: 0.2974697311097443, 4: 0.8107763105792996, 5: 0.11104358691482623, 6: 0.3759811022301596, 7: 0.1483878034498358, 8: 0.5596319388056691, 9: 0.5066800531498984, 10: 0.5150372223274877, 11: 0.27397613568260565, 12: 0.6681430770505361, 13: 0.2202571269280942, 14: 0.8475269265930389, 15: 0.9883342111664036, 16: 0.8687687919394059, 17: 0.19407288999820652, 18: 0.5323598329735986, 19: 0.8655235364863463, 20: 0.24505267899965744, 21: 0.8548754620024638, 22: 0.6243000956749827, 23: 0.8886832169059365}\n",
      "selected action:  15 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  5 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "samples_from_beta_distr {0: 0.8534632292097761, 1: 0.8456549058650626, 2: 0.6226440234734585, 3: 0.7604071335011117, 4: 0.7464760604315592, 5: 0.5541328941390731, 6: 0.714587452919431, 7: 0.5517642969734265, 8: 0.15319121909624528, 9: 0.046712227208941595, 10: 0.19135051939016406, 11: 0.6892431618109558, 12: 0.7726806107047062, 13: 0.7560286735721317, 14: 0.3142327224770532, 15: 0.3510162663959102, 16: 0.7345369873545211, 17: 0.33194573622138746, 18: 0.5547855317926836, 19: 0.6362922390397535, 20: 0.3431554611677674, 21: 0.7760357491530214, 22: 0.5461444386880181, 23: 0.8282425994498824}\n",
      "selected action:  0 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  6 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "7\n",
      "samples_from_beta_distr {0: 0.10009568481397803, 1: 0.49445288277894045, 2: 0.23132296735267407, 3: 0.39799327269981993, 4: 0.7876685930675458, 5: 0.48383594870468233, 6: 0.4178509568809341, 7: 0.020725774887517687, 8: 0.4740962119425995, 9: 0.4091610405136515, 10: 0.8128981055350568, 11: 0.11335443315433118, 12: 0.12485432589308033, 13: 0.9893161078631669, 14: 0.0190107616799375, 15: 0.958729150112901, 16: 0.42076309848976895, 17: 0.2757019773928214, 18: 0.47409949496181836, 19: 0.910071966064865, 20: 0.28908085952555, 21: 0.7127747597937003, 22: 0.9250873178268495, 23: 0.20345291085275408}\n",
      "selected action:  13 rwd:  1\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  7 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "8\n",
      "samples_from_beta_distr {0: 0.17475657180946486, 1: 0.3394505706026144, 2: 0.0630229816166578, 3: 0.8486807360911959, 4: 0.19990068116388654, 5: 0.8227753037325924, 6: 0.5012093064113542, 7: 0.32628348163044996, 8: 0.0923717024453797, 9: 0.10644935668429405, 10: 0.3691251143905988, 11: 0.4913227477834762, 12: 0.9383188488067764, 13: 0.9066044641589257, 14: 0.7472362562628098, 15: 0.22570047252504957, 16: 0.7601012383376925, 17: 0.8091394006251462, 18: 0.1887264593501605, 19: 0.31314901170090775, 20: 0.030894278387133354, 21: 0.500554096670337, 22: 0.42125407813972404, 23: 0.9023710542171819}\n",
      "selected action:  12 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  8 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 1., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 1., 1.])\n",
      "9\n",
      "samples_from_beta_distr {0: 0.4182461357852112, 1: 0.8717233165282422, 2: 0.17065981763343885, 3: 0.3587626653575337, 4: 0.13267001392508293, 5: 0.9297944500969773, 6: 0.5891235758296584, 7: 0.7760294640964338, 8: 0.8666451139843243, 9: 0.6149842797365517, 10: 0.7541722077449847, 11: 0.003973364276294723, 12: 0.4594307453693029, 13: 0.7463538786633274, 14: 0.3302501312431656, 15: 0.4929712346327029, 16: 0.947535470271262, 17: 0.6659188728980262, 18: 0.2906474931995077, 19: 0.5301169195631278, 20: 0.6373804621139697, 21: 0.4111733787462851, 22: 0.9834149778139636, 23: 0.39609386668647517}\n",
      "selected action:  22 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  9 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 1., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "10\n",
      "samples_from_beta_distr {0: 0.15927102890834435, 1: 0.9202354218280291, 2: 0.2060110686260465, 3: 0.912022654374159, 4: 0.09941422359446568, 5: 0.8522660671265331, 6: 0.9528067455698378, 7: 0.32438766653177764, 8: 0.672869037682053, 9: 0.2138615654229661, 10: 0.24820236880202787, 11: 0.24037640292649504, 12: 0.045184289945626566, 13: 0.7092507287806158, 14: 0.6406170236348596, 15: 0.2856460947983737, 16: 0.3485772095187064, 17: 0.9830664546184997, 18: 0.2878210842084993, 19: 0.4316618953217199, 20: 0.0943542706466655, 21: 0.023810674790529052, 22: 0.45413336072602445, 23: 0.07796667975252075}\n",
      "selected action:  17 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  10 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 1., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "samples_from_beta_distr {0: 0.07560291225537466, 1: 0.9495637074235239, 2: 0.95583970162349, 3: 0.12417683916624335, 4: 0.12153136866521876, 5: 0.6322180619067329, 6: 0.044841420294462565, 7: 0.8715794945090845, 8: 0.039947578372135306, 9: 0.5288274723152131, 10: 0.24653304021449354, 11: 0.1660728268754606, 12: 0.3853495498146277, 13: 0.86706447076186, 14: 0.8591851023134905, 15: 0.9729191253306909, 16: 0.6112215590216921, 17: 0.3065197409118227, 18: 0.5356356602416065, 19: 0.5805184802102746, 20: 0.8533261057548986, 21: 0.5043882948210544, 22: 0.6098662989533995, 23: 0.3275901379406602}\n",
      "selected action:  15 rwd:  1\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  11 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 1., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "12\n",
      "samples_from_beta_distr {0: 0.5273886538582511, 1: 0.7580080545992859, 2: 0.91102383419794, 3: 0.11358375461269266, 4: 0.6118004352416205, 5: 0.7696898393634597, 6: 0.3449528504232818, 7: 0.6594084749351574, 8: 0.7737003441159445, 9: 0.5957208093525199, 10: 0.8042242598573609, 11: 0.4092976474096036, 12: 0.38518120189012384, 13: 0.9977239312704619, 14: 0.2235687864072978, 15: 0.4793898657104937, 16: 0.03833167930200988, 17: 0.061061086191569865, 18: 0.5919395314401696, 19: 0.8537936312044704, 20: 0.5389796776794419, 21: 0.8642451271712965, 22: 0.9368719056849688, 23: 0.010904573472137018}\n",
      "selected action:  13 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  12 new params:  tensor([1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "13\n",
      "samples_from_beta_distr {0: 0.6453690035401887, 1: 0.9741926843456884, 2: 0.8470206440499811, 3: 0.8241986264103451, 4: 0.27062906535914777, 5: 0.36609713905413377, 6: 0.2171590323835805, 7: 0.5115840625614437, 8: 0.36015818610383177, 9: 0.0736877592362563, 10: 0.12596648604755448, 11: 0.692112564867311, 12: 0.06112709600545758, 13: 0.6520760574497853, 14: 0.9490847217566056, 15: 0.6119495177351612, 16: 0.17158569080342842, 17: 0.10416364516959607, 18: 0.9401679204658799, 19: 0.635331740637517, 20: 0.8745590923083367, 21: 0.9383119543425874, 22: 0.801104385993971, 23: 0.8824101756424314}\n",
      "selected action:  1 rwd:  1\n",
      "updated rewd vec:  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  13 new params:  tensor([1., 3., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "14\n",
      "samples_from_beta_distr {0: 0.20740959183092691, 1: 0.8077748244044012, 2: 0.507061189883096, 3: 0.3890232479337752, 4: 0.9407687069529674, 5: 0.288465364727153, 6: 0.9590796102754193, 7: 0.13057541801275807, 8: 0.7614750629243877, 9: 0.26829463996935243, 10: 0.45588161930596316, 11: 0.2856880555560867, 12: 0.31529070690177124, 13: 0.6323692630622181, 14: 0.937509816224449, 15: 0.13975927736210828, 16: 0.6414534919663248, 17: 0.34054708169152426, 18: 0.9058185921210747, 19: 0.7693243232929687, 20: 0.3990651616815434, 21: 0.46731858013123, 22: 0.46590098769600413, 23: 0.25382286218969496}\n",
      "selected action:  6 rwd:  1\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  14 new params:  tensor([1., 3., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "15\n",
      "samples_from_beta_distr {0: 0.44964461949405415, 1: 0.9346356472510663, 2: 0.45374801649262486, 3: 0.48791478376972625, 4: 0.5138469816528903, 5: 0.4394300081294314, 6: 0.9553667522129559, 7: 0.7266639132878941, 8: 0.8159337787238726, 9: 0.5622775313488113, 10: 0.5941833781222186, 11: 0.5579135265090392, 12: 0.03550896968975924, 13: 0.5220415521313233, 14: 0.7939503479697602, 15: 0.460517260983787, 16: 0.1990458160488506, 17: 0.2954967623475309, 18: 0.7511401763187405, 19: 0.275033644159545, 20: 0.5341141246814163, 21: 0.28652566727959516, 22: 0.41339598526814586, 23: 0.7565269697935043}\n",
      "selected action:  6 rwd:  1\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  15 new params:  tensor([1., 3., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "samples_from_beta_distr {0: 0.19273597147134916, 1: 0.9991380855085331, 2: 0.5118300967953122, 3: 0.0250911724231679, 4: 0.8638646792325634, 5: 0.662939070907857, 6: 0.8266347859729993, 7: 0.8361862881893927, 8: 0.19607109754866917, 9: 0.6798904856206051, 10: 0.3855871673221644, 11: 0.09365195490197975, 12: 0.14362350602729657, 13: 0.547816591501556, 14: 0.13371887782807657, 15: 0.5421610798807064, 16: 0.10568550962162152, 17: 0.24539350556809023, 18: 0.5687718540417096, 19: 0.3532765776298581, 20: 0.5124273537820075, 21: 0.9327730832490938, 22: 0.7734021713990266, 23: 0.297754546893135}\n",
      "selected action:  1 rwd:  1\n",
      "updated rewd vec:  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  16 new params:  tensor([1., 4., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "17\n",
      "samples_from_beta_distr {0: 0.36771231169940266, 1: 0.6728743539425678, 2: 0.14235939776709597, 3: 0.06355849640782012, 4: 0.4515250103378668, 5: 0.7276867574902038, 6: 0.823211718771775, 7: 0.3400950459467436, 8: 0.9924542700634675, 9: 0.20486877230882472, 10: 0.579595197402793, 11: 0.2286478674266721, 12: 0.4624503112403579, 13: 0.7300298999030167, 14: 0.49257230067686336, 15: 0.2381155549849858, 16: 0.5981315684974288, 17: 0.4518062560020568, 18: 0.1963369793446434, 19: 0.6741998984501644, 20: 0.5205330950355143, 21: 0.3155477485928485, 22: 0.5586648795098003, 23: 0.6464039172489847}\n",
      "selected action:  8 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  17 new params:  tensor([1., 4., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 1., 2., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "18\n",
      "samples_from_beta_distr {0: 0.025402401958249932, 1: 0.8317596028166049, 2: 0.2782093420607823, 3: 0.6387859711841157, 4: 0.343484085254983, 5: 0.4549894046750489, 6: 0.8028820141200722, 7: 0.9852980436354434, 8: 0.15700927218341862, 9: 0.16952474103087742, 10: 0.606780775693321, 11: 0.6079244297236994, 12: 0.4257353025121252, 13: 0.36331725316432184, 14: 0.5870914003715495, 15: 0.40787425233027685, 16: 0.34034082428651735, 17: 0.8241953041054743, 18: 0.3266282982189399, 19: 0.3512120236413617, 20: 0.278853438810696, 21: 0.6204314279108133, 22: 0.6912130648939531, 23: 0.7935762136274873}\n",
      "selected action:  7 rwd:  0\n",
      "updated rewd vec:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  18 new params:  tensor([1., 4., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 2., 2., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n",
      "19\n",
      "samples_from_beta_distr {0: 0.22967662863237545, 1: 0.9680680987520064, 2: 0.8798535851560426, 3: 0.42774803726946214, 4: 0.6098474396255916, 5: 0.6981206089292153, 6: 0.49489587667989726, 7: 0.7398711584303798, 8: 0.23550962428367675, 9: 0.23242810907919578, 10: 0.040044171579199345, 11: 0.1538208194219958, 12: 0.7277707148374857, 13: 0.6219011700417757, 14: 0.6800170161760658, 15: 0.5504181368998493, 16: 0.3308536702117983, 17: 0.5821197781204781, 18: 0.286167669734955, 19: 0.18238668254258192, 20: 0.32388687829550605, 21: 0.5827379762266875, 22: 0.7431409018593172, 23: 0.8191871829415177}\n",
      "selected action:  1 rwd:  1\n",
      "updated rewd vec:  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "time_step:  19 new params:  tensor([1., 5., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1., 1., 3., 1., 2., 1., 1.,\n",
      "        1., 1., 1., 1., 2., 1.]) tensor([2., 1., 1., 1., 1., 1., 1., 2., 2., 2., 1., 2., 2., 2., 1., 2., 1., 2.,\n",
      "        1., 1., 1., 1., 2., 1.])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "from IPython.display import clear_output\n",
    "from IPython import display\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import bernoulli\n",
    "import random\n",
    "\n",
    "num_possibilities = 24\n",
    "\n",
    "def draw_plot(_alpha_beta, time_step=8888):\n",
    "    fig = plt.figure()\n",
    "    fig.canvas.draw()\n",
    "    plt.xlim(0,1)\n",
    "    x = np.linspace(0, 1, 100)\n",
    "    tempy = []\n",
    "\n",
    "    for idx in range(len(_alpha_beta[0])):\n",
    "        a = _alpha_beta[0][idx]\n",
    "        b = _alpha_beta[1][idx]\n",
    "        y = ss.beta.pdf(x, a,b)\n",
    "        plt.plot(x,y,label=idx)\n",
    "\n",
    "    plt.ylim(0,30)\n",
    "    plt.legend()\n",
    "    plt.title('@ time_step: {}'.format(time_step))\n",
    "    display.display(plt.gcf())\n",
    "\n",
    "class simulator():\n",
    "    def __init__(self, slots=[.1, .6, .8]):\n",
    "        self.slots = slots\n",
    "        self.action_space = list(range(len(slots)))\n",
    "\n",
    "    def simulate(self, slot_idx):\n",
    "        return bernoulli.rvs(self.slots[slot_idx]) \n",
    "\n",
    "    def simulate_ui(self, slot_idx):\n",
    "        rwd = input(f'displaying with UI config {slot_idx} out of {len(self.slots)} options, input 1 for click 0 for no')\n",
    "        return rwd\n",
    "\n",
    "rand_rates = [min(random.random(), .8) - (random.random() / 100) for iter in range(num_possibilities)]\n",
    "\n",
    "print('rand_rates', rand_rates)\n",
    "\n",
    "env = simulator(rand_rates)\n",
    "env.action_space\n",
    "\n",
    "def run_simulation(n):\n",
    "    _one_vec= [1.0] * num_possibilities\n",
    "    _blank_vec= [0.0] * num_possibilities\n",
    "    alphas = th.tensor(_one_vec, requires_grad=False)\n",
    "    betas = th.tensor(_one_vec, requires_grad=False)\n",
    "\n",
    "    samples_from_beta_distr = {}\n",
    "    time_step = 0\n",
    "\n",
    "    for x in range(n):\n",
    "        print(x)\n",
    "        rwd_vec = _blank_vec[:]\n",
    "        sampled_vec = _blank_vec[:]\n",
    "        for k in range(num_possibilities):#env.action_space:\n",
    "            samples_from_beta_distr[k] = np.random.beta(alphas[k], betas[k])\n",
    "\n",
    "        print('samples_from_beta_distr', samples_from_beta_distr)\n",
    "\n",
    "        selected_action = max(samples_from_beta_distr, key=samples_from_beta_distr.get)\n",
    "        reward = env.simulate(selected_action)\n",
    "        time_step += 1\n",
    "\n",
    "        print('selected action: ', selected_action, 'rwd: ',  reward)\n",
    "\n",
    "        rwd_vec[selected_action] = float(reward)\n",
    "        sampled_vec[selected_action] = 1\n",
    "\n",
    "        print('updated rewd vec: ', rwd_vec)\n",
    "\n",
    "        (alphas, betas) = bandit_thompson(th.tensor(rwd_vec),th.tensor(sampled_vec), alphas, betas)\n",
    "\n",
    "        print('time_step: ', x, 'new params: ', alphas, betas)\n",
    "\n",
    "        if x % 5 == 0:\n",
    "            draw_plot((alphas, betas), x)\n",
    "\n",
    "    return (alphas, betas)\n",
    "\n",
    "''' thompson sampling bandit '''\n",
    "# the first elem of alpha_beta is the alpha parameter for a beta distr for i-th option where i = index\n",
    "# the 2nd elem of alpha_beta is the beta parameter ... \n",
    "# alphabeta will never be more than 2 elements but each alpha and beta vector could have more elements if we have more options\n",
    "# this organization allows us to do vectorized updating of the params\n",
    "\n",
    "_blank_vec= [0.0] * num_possibilities\n",
    "_one_vec = [1.0] * num_possibilities\n",
    "\n",
    "print(_blank_vec, len(_blank_vec))\n",
    "\n",
    "alphas = th.tensor(_one_vec, requires_grad=False)\n",
    "betas = th.tensor(_one_vec, requires_grad=False)\n",
    "\n",
    "rwd = th.tensor(_blank_vec)\n",
    "samples = th.tensor(_blank_vec)\n",
    "bandit_args_th = [rwd, samples, alphas, betas]\n",
    "bandit_th_args_shape = [rwd.shape, samples.shape, alphas.shape, betas.shape]\n",
    "\n",
    "@sy.func2plan(args_shape=bandit_th_args_shape)\n",
    "def bandit_thompson(reward, sample_vector, alphas, betas):\n",
    "    prev_alpha = alphas\n",
    "    prev_beta = betas\n",
    "\n",
    "    alphas = prev_alpha.add(reward)\n",
    "    betas = prev_beta.add(sample_vector.sub(reward))\n",
    "\n",
    "    return (alphas, betas)\n",
    "        \n",
    "final_alphas, final_betas = run_simulation(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1) tensor(0)\n",
      "tensor(0) tensor(1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7292068670766164"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check\n",
    "print(np.argmax(final_alphas), np.argmax(final_betas))\n",
    "print(np.argmin(final_alphas), np.argmin(final_betas))\n",
    "\n",
    "rand_rates[18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Define Averaging Plan\n",
    "\n",
    "Averaging Plan is executed by PyGrid at the end of the cycle,\n",
    "to average _diffs_ submitted by workers and update the model\n",
    "and create new checkpoint for the next cycle.\n",
    "\n",
    "_Diff_ is the difference between client-trained\n",
    "model params and original model params,\n",
    "so it has same number of tensors and tensor's shapes\n",
    "as the model parameters.\n",
    "\n",
    "We define Plan that processes one diff at a time.\n",
    "Such Plans require `iterative_plan` flag set to `True`\n",
    "in `server_config` when hosting FL model to PyGrid.\n",
    "\n",
    "Plan below will calculate simple mean of each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "@sy.func2plan()\n",
    "def avg_plan(avg, item, num):\n",
    "    new_avg = []\n",
    "\n",
    "    for i, param in enumerate(avg):\n",
    "        new_avg.append((avg[i] * num + item[i]) / (num + 1))\n",
    "        \n",
    "    return new_avg\n",
    "\n",
    "# Build the Plan\n",
    "_ = avg_plan.build(bandit_args_th, bandit_args_th\n",
    ", th.tensor([1.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def avg_plan(arg_1, arg_2, arg_3, arg_4, arg_5, arg_6, arg_7, arg_8, arg_9):\n",
      "    var_0 = arg_1.__mul__(arg_9)\n",
      "    var_1 = var_0.__add__(arg_5)\n",
      "    var_2 = arg_9.__add__(1)\n",
      "    out_1 = var_1.__truediv__(var_2)\n",
      "    var_3 = arg_2.__mul__(arg_9)\n",
      "    var_4 = var_3.__add__(arg_6)\n",
      "    var_5 = arg_9.__add__(1)\n",
      "    out_2 = var_4.__truediv__(var_5)\n",
      "    var_6 = arg_3.__mul__(arg_9)\n",
      "    var_7 = var_6.__add__(arg_7)\n",
      "    var_8 = arg_9.__add__(1)\n",
      "    out_3 = var_7.__truediv__(var_8)\n",
      "    var_9 = arg_4.__mul__(arg_9)\n",
      "    var_10 = var_9.__add__(arg_8)\n",
      "    var_11 = arg_9.__add__(1)\n",
      "    out_4 = var_10.__truediv__(var_11)\n",
      "    return out_1, out_2, out_3, out_4\n"
     ]
    }
   ],
   "source": [
    "# Let's check Plan contents\n",
    "print(avg_plan.code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Test averaging plan\n",
    "# Pretend there're diffs, all params of which are ones * dummy_coeffs\n",
    "dummy_coeffs = [1, 5.5, 7, 55]\n",
    "dummy_diffs = [[th.ones_like(param) * i for param in bandit_args_th] for i in dummy_coeffs]\n",
    "mean_coeff = th.tensor(dummy_coeffs).mean().item()\n",
    "\n",
    "# Remove original function to make sure we execute traced Plan\n",
    "avg_plan.forward = None\n",
    "\n",
    "# Calculate avg value using our plan\n",
    "avg = dummy_diffs[0]\n",
    "\n",
    "for i, diff in enumerate(dummy_diffs[1:]):\n",
    "    avg = avg_plan(list(avg), diff, th.tensor([i + 1]))\n",
    "\n",
    "# Avg should be ones*mean_coeff for each param\n",
    "for i, param in enumerate(bandit_args_th):\n",
    "    expected = th.ones_like(param) * mean_coeff\n",
    "    assert avg[i].eq(expected).all(), f\"param #{i}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Step 4: Host in PyGrid\n",
    "\n",
    "Let's now host everything in PyGrid so that it can be accessed by worker libraries (syft.js, KotlinSyft, SwiftSyft, or even PySyft itself).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Follow PyGrid [README](https://github.com/OpenMined/PyGrid/#getting-started) to start PyGrid Node. In the code below we assume that the PyGrid Node is running on `127.0.0.1`, port `5000`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Define name, version, configs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PyGrid Node address\n",
    "gridAddress = \"127.0.0.1:5000\"\n",
    "grid = ModelCentricFLClient(id=\"test\", address=gridAddress, secure=False)\n",
    "grid.connect()\n",
    "\n",
    "# These are the name/version you use in worker\n",
    "name = \"bandit\"\n",
    "version = \"1.0.0\"\n",
    "\n",
    "client_config = {\n",
    "    \"name\": name,\n",
    "    \"version\": version,\n",
    "    \"batch_size\": 64,\n",
    "    \"lr\": 0.005,\n",
    "    \"max_updates\": 100  # custom syft.js option that limits number of training loops per worker\n",
    "}\n",
    "\n",
    "server_config = {\n",
    "    \"min_workers\": 1,\n",
    "    \"max_workers\": 1,\n",
    "    \"pool_selection\": \"random\",\n",
    "    \"do_not_reuse_workers_until_cycle\": 20,\n",
    "    \"cycle_length\": 28800,  # max cycle length in seconds\n",
    "    \"num_cycles\": 200,  # max number of cycles\n",
    "    \"max_diffs\": 1,  # number of diffs to collect before avg\n",
    "    \"minimum_upload_speed\": 0,\n",
    "    \"minimum_download_speed\": 0,\n",
    "    \"iterative_plan\": True  # tells PyGrid that avg plan is executed per diff\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Host response: {'type': 'model-centric/host-training', 'data': {'status': 'success'}}\n"
     ]
    }
   ],
   "source": [
    "model_params_state = State(\n",
    "    state_placeholders=[\n",
    "        PlaceHolder().instantiate(param)\n",
    "        for param in bandit_args_th\n",
    "    ]\n",
    ")\n",
    "\n",
    "response = grid.host_federated_training(\n",
    "    model=model_params_state,\n",
    "    client_plans={'training_plan': bandit_thompson},\n",
    "    client_protocols={},\n",
    "    server_averaging_plan=avg_plan,\n",
    "    client_config=client_config,\n",
    "    server_config=server_config\n",
    ")\n",
    "\n",
    "print(\"Host response:\", response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you see `status: success` this means the plan is successfully hosted in the PyGrid!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Step 5: Train\n",
    "\n",
    "To train hosted model, use the multi-armed bandit example in [syft.js](https://github.com/OpenMined/syft.js/tree/master/examples/multi-armed-bandit)."
   ]
  }
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